Field
The present invention relates to the field of automated, computerized driver assistance for vehicles. The invention regards in particular a method and corresponding program for computationally predicting a future movement behaviour of a target vehicle taking into account a global scene context, the vehicle equipped with such system and the system itself.
Description of the Related Art
An automated driver assistance system in a vehicle in most cases includes a sensor physically sensing the environment of the vehicle, and a computing unit, supplied with a sensor output signal, and computing an output signal which assists the driver in the guidance of the vehicle. The output signal may be supplied to optical or acoustical indication means and/or to a controller unit for controlling an actuator of the vehicle. An actuator of the vehicle might be a part of safety device, for example an inflator for an airbag system, or an actuator influencing the movement of the vehicle, for example brakes, accelerator, steering.
Driver assistance systems such as an “Adaptive Cruise Control” (ACC) system (for example described in ISO-Norm 15622:2010) increase driver comfort and safety. ACC systems are especially used for carrying out longitudinal control of a host vehicle, for example with respect to a target speed specified by the driver and ranging to other traffic objects such as other vehicles, for example cars, motorbikes, bikes, trucks or pedestrians. Driver assistance systems, for example lane change assistants, base on predicting a future behaviour of other participants in a traffic environment of a vehicle with aid of a prediction system.
The term “host vehicle” will be used in the following elaboration for a vehicle in a traffic situation which has a prediction system according to the invention mounted thereon and which is equipped with the sensors and/or further means for acquiring traffic related data and a computing system that allows the computation of a possible or likely future behaviour of at least one other traffic vehicle. The host vehicle is sometimes also referenced as ego-vehicle.
A sensor may be any means that can deliver information suitable for describing a traffic scene at a point of time by physically sensing an environment of the host vehicle. Such sensors may be cameras, radar, lidar or the like. A data acquisition means forms part of the driver assistance system for acquiring any kind of information describing the traffic scene provided by one or more sensors our other data sources.
A target object or target vehicle is an object or a vehicle for which a future behaviour shall be estimated. The target object may be observed by the sensor(s) or information regarding the target object/vehicle may be obtained by the data acquisition means from other data sources.
One problem of commonly known driver assistance systems is that usually the system only takes into consideration behaviour of other traffic vehicles which is recognized by physical prediction. If for example one of the target objects (a vehicle in the environment of the host vehicle and physically sensed by one or more sensors of the host vehicle) changes the lane thereby cutting the lane of the host vehicle or cutting out to another lane, the host vehicle observing the other traffic objects only reacts after such lane change is certain. In consequence the reaction of the host vehicle is delayed which has a negative influence on the traffic flow and in particular on the comfort of the driver of the host vehicle.
Physical prediction relies on direct indicators which provide variables, which are observable if and only if the behaviour to be detected has already started. For example for predicting a lane-change, direct indicators are lateral velocity, lateral position relative to the lane, changing orientation relative to the lane, or changing orientation relative to other traffic participants.
EP 2 562 060 A1 proposes to use in addition to such physical prediction process a so-called context-based prediction. In such context-based prediction a probability of the future movement behaviour of a target object is calculated. This future behaviour is then either confirmed or dismissed by the result of the physical prediction. For calculating the probability of a possible future movement behaviour of the target objects, indirect indicators are used which do not only allow the observation of a behaviour of the target object when it already has started but give information on the traffic scene in which a future movement behaviour shall be predicted.
Indirect indicators provide variables, which are already observable before the to be predicted future movement behaviour starts. We define indirect indicators as the set of all possible indicators without the set of direct indicators.
From the calculated probabilities for future movement behaviour of the target object, it is possible to recognize traffic situations that make a particular behaviour of the target objects likely to happen.
Publication by Bonnin, S; Weisswange, T.; Kummerich F. and Schmuedderich, J.: General behaviour prediction by a combination of scenario-specific models, in: IEEE transactions on intelligent transportation systems, volume PP, issue 99, pages 1-11: IEEE 2014, ISSN 1524-9050 discusses a combination of general specific classifiers to improve quality and scope based on a concrete implementation for lane-change prediction in highway scenarios.
One problem of such approach as followed in reference EP 2 562 060 A1 is that for context-based prediction only the current situation of the traffic situation of the host vehicle is taken into consideration. Thus, at a particular point in time the context-based prediction may for example give the result that a target vehicle which is on a neighboring lane will cut in the lane of the host vehicle because the target vehicle quickly approaches a predecessor vehicle driving on the same lane as the target vehicle. According to EP 2 562 060 A1, this will result in deceleration of the host vehicle, because in such a situation it is likely that the target vehicle will cut in to overtake the slower predecessor vehicle.
But the traffic scene that is the basis for such estimation can be characterized by further characteristics. The road on which the host vehicle as well as the target vehicle are travelling might either be crowded due to many vehicles cruising only at minimum inter-vehicle distances into the same driving direction, or might alternatively only be populated by a small number of vehicles cruising with large inter-vehicle distances.
The known context-based prediction nevertheless provides results independent from any traffic density.
An example of a situation where the known context-based prediction comes to its limits shall be explained with reference to FIGS. 1A and 1B.
In FIGS. 1A and 1B the behaviour of a vehicle A as a target vehicle is to be predicted. The vehicle A approaches vehicle B also driving on the center lane of a road with multiple lanes, wherein vehicle B is cruising with a velocity smaller than the velocity of vehicle A. Throughout the annexed figures the length of solid line arrows indicate the current velocity of the respective vehicle. A behaviour prediction for vehicle A as the target vehicle in the illustrated situation of approaching its predecessor determines a gap on the left line of the three-lane road, but the gap does not fit well, as a vehicle E (host vehicle) at the rear end of the gap is approaching with a velocity significantly larger than the velocity of the target vehicle A.
In FIG. 1A the road is crowded by many vehicles at only small inter-vehicle distances driving into the same direction as vehicle A. Vehicle A is shown to change lane in front of host vehicle E, although the determined gap is relatively small. However, a driver of vehicle A presumably will not see better future chances for changing to the left lane instead of decelerating and thus the probability of a lane change even into a relative small gap increases.
In FIG. 1B the traffic scene shows only some vehicles at generally large inter-vehicle distances, the vehicles driving into the same direction as vehicle A. Vehicle A is shown to change lane behind host vehicle E, but the sparsely populated road enables to proceed as depicted contrary to the scene depicted in FIG. 1A.
In the state of the art prediction system, a predicted behaviour of a target vehicle is estimated to be different, when the local context of the traffic situation is different. The local context is different if at least one of the other traffic participants (meaning not the host vehicle E or target vehicle A) or the recognized infrastructure in the ego-vehicle's direct vicinity is different. For example other traffic participants such as predecessor vehicles differ or a highway has an entrance or exit in the surroundings taken into account. Accordingly the available situation models for a context-based prediction are activated or selected for example based on                a predicted behaviour applicable in the detected scene, such as “cut-in” or “cut-out”, or        a detected constellation of traffic participants, e.g. two vehicles on a neighbouring lane, or        a recognized infrastructure elements, e.g. a highway entrance in driving direction.        
However although the recognized elements define a same situation, the driving behaviour of a real driver of a target vehicle might nevertheless differ. So in FIG. 1A the target vehicle A will change from the center lane to the left lane despite the gap fitting badly due to the fact that the driver is aware of a highly crowded highway and the probability of a better fitting gap is only low. If the highway is almost empty the same traffic situation of vehicles A, E and B with their respective velocities and spatial distribution according to FIG. 1B, the driver of vehicle A will most probably wait for a more suitable gap to change lane. However when taking the traditional predictive movement behaviour methods into consideration, in both cases shown in FIGS. 1A, 1B either a cut-in or no cut-in is predicted, as for both cases the local context of the prediction for the behaviour of vehicle A is the same. However the target vehicle A will act differently in the situations depicted in FIG. 1A and in FIG. 1B.
Similar problems as discussed with respect to traffic density and behaviour prediction arise with respect to general speed limits in countries or with a state of the road, e.g. if a road is icy. The implications will be discussed in more detail with respect to FIGS. 2A, 2B, 3A, and 3B later.
Therefore an inaccuracy in predicting the target vehicle's behaviour inherent to state of the art context-based prediction systems is to be stated.
According to the existing proposals, only the local context of the actual traffic situation is taken into consideration when estimating a future behaviour of target objects. The quality of the recommended action of the prediction of a prediction system is therefore limited.